Structural brain network differences in bipolar disorder using with similarity-based approach

Toward individualized connectomes of brain morphology

The morphological brain connectome (MBC) delineates the coordinated patterns of local morphological features (such as cortical thickness) across brain regions. While classically constructed using population-based approaches, there is a growing trend toward individualized modeling. Currently, the methods for individualized MBCs are varied, posing challenges for method selection and cross-study comparisons. Here, we summarize how individualized MBCs are modeled through low-order methods (correlation-, divergence-, distance-, and deviation-based methods) describing relations in brain morphology, as well as high-order methods capturing similarities in these low-order relations. We discuss the merits and limitations of different methods, examining them in the context of robustness, reproducibility, and reliability. We highlight the importance of elucidating the cellular and molecular mechanisms underlying the individualized connectome, and establishing normative benchmarks to assess individual variation in development, aging, and neuropsychiatric disorders.

Age and Sex-Related Effects on Single-Subject Gray Matter Networks in Healthy Participants

Recent developments in image analysis have enabled an individual’s brain network to be evaluated and brain age to be predicted from gray matter images. Our study aimed to investigate the effects of age and sex on single-subject gray matter networks using a large sample of healthy participants. We recruited 812 healthy individuals (59.3 ± 14.0 years, 407 females, and 405 males) who underwent three-dimensional T1-weighted magnetic resonance imaging. Similarity-based gray matter networks were constructed, and the following network properties were calculated: normalized clustering, normalized path length, and small-world coefficients. The predicted brain age was computed using a support-vector regression model. We evaluated the network alterations related to age and sex. Additionally, we examined the correlations between the network properties and predicted brain age and compared them with the correlations between the network properties and chronological age. The brain network retained efficient small-world properties regardless of age; however, reduced small-world properties were observed with advancing age. Although women exhibited higher network properties than men and similar age-related network declines as men in the subjects aged < 70 years, faster age-related network declines were observed in women, leading to no differences in sex among the participants aged ≥ 70 years. Brain age correlated well with network properties compared to chronological age in participants aged ≥ 70 years. Although the brain network retained small-world properties, it moved towards randomized networks with aging. Faster age-related network disruptions in women were observed than in men among the elderly. Our findings provide new insights into network alterations underlying aging.

Voxel-based correlation of 18F-THK5351 accumulation with gray matter structural networks in cognitively normal older adults

Objective

The aim of this study was to evaluate tau-related structural network metrics derived from gray matter magnetic resonance imaging (MRI) scans in cognitively normal (CN) older adults.

Methods

We recruited 47 amyloid-negative CN older adults (mean age ± standard deviation, 65.0 ± 7.9 years; 26 women). All participants underwent 3D T1-weighted MRI and ¹¹C-Pittsburgh compound-B and ¹⁸F-THK5351 positron emission tomography scans. Four local network metrics (betweenness centrality, clustering coefficient, characteristic path length, and degree) were computed and rendered on individual brain images. We then evaluated the correlations between ¹⁸F-THK5351 positron emission tomography images and local network metric images at the voxel level.

Results

Significant positive correlations of the four local network metrics with ¹⁸F-THK5351 were detected in the bilateral caudate.

Conclusion

Our findings suggest that tau and neuroinflammation in CN older adults may influence the gray matter structural network in the caudate.

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Local correlation of ¹⁸F-THK5351 and network metrics images in healthy elderly.

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Positive correlation between ¹⁸F-THK5351and network changes in the caudate.

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Tau and neuroinflammation may influence structural network in the caudate.